DeepAI AI Chat
Log In Sign Up

Adaptive Region-Based Active Learning

by   Corinna Cortes, et al.

We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active learning baselines.


page 1

page 2

page 3

page 4


Active learning using region-based sampling

We present a general-purpose active learning scheme for data in metric s...

Flattening a Hierarchical Clustering through Active Learning

We investigate active learning by pairwise similarity over the leaves of...

Active learning algorithm through the lens of rejection arguments

Active learning is a paradigm of machine learning which aims at reducing...

ED2: Two-stage Active Learning for Error Detection – Technical Report

Traditional error detection approaches require user-defined parameters a...

Target-Independent Active Learning via Distribution-Splitting

To reduce the label complexity in Agnostic Active Learning (A^2 algorith...

Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis

The flare-productivity of an active region is observed to be related to ...

Near Optimal Bayesian Active Learning for Decision Making

How should we gather information to make effective decisions? We address...